The MONROE testbed enables the objective performance assessment of MBB networks from the end-user perspective, using highly distributed measurements from fixed and mobile nodes. To quantify the performance of MBB networks for popular Internet services from a user-centric perspective, dedicated tools are needed. In this paper we extend the MONROE testbed to the Quality of Experience (QoE) domain, presenting the design and implementation of a QoE-capable measurement tool for YouTube video streaming. The measurement concept is based on emulating a virtual end-user device requesting video streams, which are then monitored at the network and application layers, on the basis of QoE-relevant features. The initial measurements conducted in the MONROE testbed and reported in this paper demonstrate the applicability of the implemented measurement concept.

Monitoring the Quality of Experience (QoE) undergone by cellular network customers has become paramount for cellular ISPs, who need to ensure high quality levels to limit customer churn due to quality dissatisfaction. This paper tackles the problem of QoE monitoring, assessment and prediction in cellular networks, relying on end-user device (i.e., smartphone) QoS passive traffic measurements and QoE crowdsourced feedback. We conceive different QoE assessment models based on supervised machine learning techniques, which are capable to predict the QoE experienced by the end user of popular smartphone apps (e.g., YouTube and Facebook), using as input the passive in-device measurements. Using a rich QoE dataset derived from field trials in operational cellular networks, we benchmark the performance of multiple machine learning based predictors, and construct a decision-tree based model which is capable to predict the per-user overall experience and service acceptability with a success rate of 91% and 98% respectively. To the best of our knowledge, this is the first paper using end-user, in-device passive measurements and machine learning models to predict the QoE of smartphone users in operational cellular networks.

WhatsApp is a very popular mobile messaging application, which dominates today’s mobile communication. Especially the feature of group chats contributes to its success and changes the way people communicate. The group-based communication paradigm is investigated in this work, particularly focusing on the usage of WhatsApp, communication in group chats, and implications on mobile network traffic.

This work investigates group-based communication in WhatsApp based on a survey and the analysis of messaging logs. The characteristics of WhatsApp group chats in terms of usage and topics are outlined. We present a classification based on the topic of the group and classify anonymized messaging logs based on message statistics. Finally, we model WhatsApp group communication with a semi-Markov process, which can be used to generate network traffic similar to real messaging logs.